Human Electroencephalographic Biometric Person Recognition System

Abstract

Human head generates various signals according to the situation and activates inside the head as well as outside the head. The frequency of the Head Signal means that brain signal is different as per the level of action taken place by the person; it may be either be imaginary or motor imagery activities. From the brain signals imaginary signals are captured using MindWave Mobile Portable device. Frequency-wise channels are separated and categories as Delta, Theta, Alpha, and Beta. These channels indicated emotions, movement, sensations, vision, etc. Features are extracted of each channel using Power Spectral Density (PSD) function and Deep learning Neural Network. Feature level fusion is used for pattern matching. The Novelty of this work is a single electrode device that is used to capture an Electroencephalography (EEG) imaginary data from the head which is generated by brain functioning. The feature level fusion of channels and Deep learning Neural Network classification of feature give better performance. The results are proven that these EEG imaginary signals could be used as better biometrics-based authentication system.